Classification of Individuals with Complex Structure

This paper introduces a foundation for inductive learning based on the use of higher-order logic for knowledge representation. In particular, the paper (i) provides a systematic individuals-as-terms approach to knowledge representation for inductive learning, and demonstrates the utility of types and higherorder constructs for this purpose; (ii) introduces a systematic way to construct predicates for use in induced definitions; and (iii) widens the applicability of decision-tree algorithms beyond the usual attribute-value setting to the classification of individuals with complex internal structure. The paper contains several illustrative applications. The effectiveness of the approach is demonstrated by applying the decision-tree learning system to two benchmark problems.

• Type: Conference
• Year: 2000
• Where: ICML
• Authors: Antony F. Bowers, Christophe G. Giraud-Carrier, John W. Lloyd
• Keywords: ICML 2000, Inductive Learning, Knowledge Representation, Machine Learning, Systematic Individuals-as-terms Approach